from __future__ import annotations

import csv
import json
import os
import re
from pathlib import Path

import torch.distributed as dist

import hackable  # noqa: F401
from hackable.utils import resolve_repo_path
from eval_sweep_models import (
    _init_distributed,
    _load_yaml,
    _resolve_local_model_dir,
    evaluate_one_model,
)


def _parse_checkpoint_step(dirname: str) -> int | None:
    m = re.match(r"^checkpoint-(\d+)$", dirname)
    if m:
        return int(m.group(1))
    m = re.search(r"-step-(\d+)$", dirname)
    if m:
        return int(m.group(1))
    return None


def _discover_checkpoint_jobs(
    base_cfg: dict, permanent_root: Path, run_label: str
) -> list[tuple[str, int, str, Path, str]]:
    """(run_label, step, resolved_model_dir_str, resolved_path, dir_name)"""
    root = permanent_root.resolve()
    if not root.is_dir():
        raise FileNotFoundError(f"Not a directory: {root}")
    jobs: list[tuple[str, int, str, Path, str]] = []
    for p in sorted(root.iterdir()):
        if not p.is_dir():
            continue
        step = _parse_checkpoint_step(p.name)
        if step is None:
            continue
        resolved = _resolve_local_model_dir(base_cfg, str(p))
        jobs.append((run_label, step, str(resolved), resolved, p.name))
    jobs.sort(key=lambda x: (x[1], x[4]))
    return jobs


def _line_chart_svg(
    series: list[tuple[str, list[tuple[int, float]], str]],
    title: str,
    y_label: str,
    y_max: float,
    path: Path,
) -> None:
    width = 900
    height = 420
    lm, rm, tm, bm = 70, 40, 50, 55
    pw = width - lm - rm
    ph = height - tm - bm
    yb = tm + ph

    all_steps: list[int] = []
    for _, pts, _ in series:
        all_steps.extend(s for s, _ in pts)
    if not all_steps:
        path.write_text(
            f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">'
            f'<text x="40" y="40">{title} (no data)</text></svg>',
            encoding="utf-8",
        )
        return
    x_min, x_max = min(all_steps), max(all_steps)
    if x_max == x_min:
        x_max = x_min + 1

    def sx(x: int) -> int:
        return lm + int((x - x_min) / (x_max - x_min) * pw)

    def sy(y: float) -> int:
        y = max(0.0, min(y_max, y))
        return yb - int((y / y_max) * ph) if y_max > 0 else yb

    parts: list[str] = [
        f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">',
        '<rect width="100%" height="100%" fill="#ffffff"/>',
        f'<text x="{lm}" y="28" font-size="16" font-family="sans-serif">{title}</text>',
        f'<text x="20" y="{tm + ph // 2}" font-size="12" font-family="sans-serif" '
        f'transform="rotate(-90 20 {tm + ph // 2})">{y_label}</text>',
        f'<line x1="{lm}" y1="{yb}" x2="{lm + pw}" y2="{yb}" stroke="#111" stroke-width="2"/>',
        f'<line x1="{lm}" y1="{tm}" x2="{lm}" y2="{yb}" stroke="#111" stroke-width="2"/>',
        f'<text x="{lm + pw // 2}" y="{height - 12}" text-anchor="middle" '
        f'font-size="12" font-family="sans-serif">Training step</text>',
    ]

    for i in range(5):
        val = (i / 4) * y_max
        yy = sy(val)
        parts.append(
            f'<line x1="{lm - 4}" y1="{yy}" x2="{lm}" y2="{yy}" stroke="#999"/>'
        )
        parts.append(
            f'<text x="{lm - 8}" y="{yy + 4}" text-anchor="end" font-size="10" '
            f'font-family="sans-serif">{val:.2f}</text>'
        )

    legend_x = lm + pw - 200
    legend_y = tm + 8
    for idx, (name, pts, color) in enumerate(series):
        if len(pts) < 2:
            pts_sorted = sorted(pts, key=lambda z: z[0])
            if not pts_sorted:
                continue
            cx, cy = sx(pts_sorted[0][0]), sy(pts_sorted[0][1])
            parts.append(
                f'<circle cx="{cx}" cy="{cy}" r="4" fill="{color}" stroke="#111"/>'
            )
        else:
            pts_sorted = sorted(pts, key=lambda z: z[0])
            d = "M " + " L ".join(f"{sx(s)} {sy(v)}" for s, v in pts_sorted)
            parts.append(
                f'<path d="{d}" fill="none" stroke="{color}" stroke-width="2.5"/>'
            )
        parts.append(
            f'<rect x="{legend_x}" y="{legend_y + idx * 18}" width="10" height="10" fill="{color}"/>'
        )
        parts.append(
            f'<text x="{legend_x + 16}" y="{legend_y + idx * 18 + 9}" font-size="11" '
            f'font-family="sans-serif">{name}</text>'
        )

    parts.append("</svg>")
    path.write_text("\n".join(parts), encoding="utf-8")


def _scatter_accuracy_vs_cot_svg(rows: list[dict], path: Path, title: str) -> None:
    """Scatter: x = avg_cot_words, y = accuracy. One color per ``run_label``; optional path by training step."""
    width = 640
    height = 520
    lm, rm, tm, bm = 72, 160, 52, 64
    pw = width - lm - rm
    ph = height - tm - bm
    yb = tm + ph

    if not rows:
        path.write_text(
            f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">'
            f'<text x="40" y="40">{title} (no data)</text></svg>',
            encoding="utf-8",
        )
        return

    labels: list[str] = []
    seen: set[str] = set()
    for r in rows:
        lab = str(r.get("run_label", "run"))
        if lab not in seen:
            seen.add(lab)
            labels.append(lab)

    colors = ["#2563eb", "#dc2626", "#16a34a", "#9333ea", "#ca8a04", "#0891b2"]
    color_map = {lab: colors[i % len(colors)] for i, lab in enumerate(labels)}

    xs = [float(r["avg_cot_words"]) for r in rows]
    ys = [float(r["accuracy"]) for r in rows]
    x_min, x_max = min(xs), max(xs)
    y_min, y_max = 0.0, 1.0
    if x_max <= x_min:
        x_max = x_min + 1.0
    pad = (x_max - x_min) * 0.06 + 1.0
    x_min = max(0.0, x_min - pad)
    x_max = x_max + pad

    def sx(x: float) -> float:
        return lm + (x - x_min) / (x_max - x_min) * pw

    def sy(y: float) -> float:
        y = max(y_min, min(y_max, y))
        return yb - (y - y_min) / (y_max - y_min) * ph

    parts: list[str] = [
        f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">',
        '<rect width="100%" height="100%" fill="#fafafa"/>',
        f'<text x="{lm}" y="30" font-size="15" font-family="sans-serif">{title}</text>',
        f'<text x="{width // 2}" y="{height - 18}" text-anchor="middle" font-size="12" '
        f'font-family="sans-serif">Avg CoT length (words)</text>',
        f'<text x="18" y="{tm + ph // 2}" font-size="12" font-family="sans-serif" '
        f'transform="rotate(-90 18 {tm + ph // 2})">Accuracy</text>',
        f'<line x1="{lm}" y1="{yb}" x2="{lm + pw}" y2="{yb}" stroke="#111" stroke-width="2"/>',
        f'<line x1="{lm}" y1="{tm}" x2="{lm}" y2="{yb}" stroke="#111" stroke-width="2"/>',
    ]

    for i in range(5):
        val = y_min + (i / 4) * (y_max - y_min)
        yy = sy(val)
        parts.append(f'<line x1="{lm - 4}" y1="{yy}" x2="{lm}" y2="{yy}" stroke="#bbb"/>')
        parts.append(
            f'<text x="{lm - 8}" y="{yy + 4}" text-anchor="end" font-size="10" '
            f'font-family="sans-serif">{val:.2f}</text>'
        )

    for i in range(5):
        frac = i / 4
        xv = x_min + frac * (x_max - x_min)
        xx = sx(xv)
        parts.append(f'<line x1="{xx}" y1="{yb}" x2="{xx}" y2="{yb + 4}" stroke="#bbb"/>')
        parts.append(
            f'<text x="{xx}" y="{yb + 18}" text-anchor="middle" font-size="10" '
            f'font-family="sans-serif">{xv:.0f}</text>'
        )

    for lab in labels:
        sub = [r for r in rows if str(r.get("run_label", "run")) == lab]
        sub.sort(key=lambda r: int(r["checkpoint_step"]))
        color = color_map[lab]
        if len(sub) >= 2:
            d = "M " + " L ".join(f'{sx(float(r["avg_cot_words"])):.1f} {sy(float(r["accuracy"])):.1f}' for r in sub)
            parts.append(
                f'<path d="{d}" fill="none" stroke="{color}" stroke-width="1.5" stroke-opacity="0.35"/>'
            )

    for r in rows:
        lab = str(r.get("run_label", "run"))
        color = color_map[lab]
        cx = sx(float(r["avg_cot_words"]))
        cy = sy(float(r["accuracy"]))
        step = int(r["checkpoint_step"])
        name = str(r.get("checkpoint_dir", f"step-{step}"))
        tip = f"{name}: accuracy={float(r['accuracy']):.4f}, avg_cot_words={float(r['avg_cot_words']):.2f}"
        parts.append(
            f'<g><circle cx="{cx:.1f}" cy="{cy:.1f}" r="5" fill="{color}" stroke="#111" stroke-width="1">'
            f"<title>{tip}</title></circle>"
            f'<text x="{cx + 8:.1f}" y="{cy - 6:.1f}" font-size="9" font-family="sans-serif" fill="#333">{step}</text></g>'
        )

    legend_x = lm + pw + 14
    legend_y = tm + 4
    parts.append(
        f'<text x="{legend_x}" y="{legend_y}" font-size="11" font-family="sans-serif" font-weight="bold">Series</text>'
    )
    for idx, lab in enumerate(labels):
        cy = legend_y + 18 + idx * 20
        parts.append(
            f'<rect x="{legend_x}" y="{cy - 8}" width="10" height="10" fill="{color_map[lab]}"/>'
        )
        parts.append(
            f'<text x="{legend_x + 16}" y="{cy}" font-size="11" font-family="sans-serif">{lab}</text>'
        )

    parts.append("</svg>")
    path.write_text("\n".join(parts), encoding="utf-8")


def _resolve_out_root(default: Path) -> Path:
    raw = os.environ.get("OUT_ROOT")
    if raw is None or not str(raw).strip():
        return resolve_repo_path(str(default))
    return resolve_repo_path(raw)


def main() -> None:
    rank, _, _ = _init_distributed()
    base_cfg = _load_yaml(str(resolve_repo_path(os.environ["BASE_CONFIG"])))

    eval_max_samples = int(os.environ.get("EVAL_MAX_SAMPLES", "200"))
    eval_batch_size = int(os.environ.get("EVAL_BATCH_SIZE", "4"))
    rollout_n = int(os.environ.get("ROLLOUT_SAMPLES", "8"))

    permanent_root = os.environ.get("PERMANENT_ROOT", "").strip()
    if permanent_root:
        pr = resolve_repo_path(permanent_root)
        run_label_single = os.environ.get("RUN_LABEL", "permanent")
        out_default = pr / "eval_permanent"
        out_root = _resolve_out_root(out_default)
        jobs_single = _discover_checkpoint_jobs(base_cfg, pr, run_label_single)
        all_jobs = jobs_single
        jobs_cw1: list = []
        jobs_cw5: list = []
    else:
        cw1_root = resolve_repo_path(os.environ["PERMANENT_CW1"])
        cw5_root = resolve_repo_path(os.environ["PERMANENT_CW5"])
        out_default = cw1_root.parent / "eval_permanent"
        out_root = _resolve_out_root(out_default)
        jobs_cw1 = _discover_checkpoint_jobs(base_cfg, cw1_root, "correctness_weight_1")
        jobs_cw5 = _discover_checkpoint_jobs(base_cfg, cw5_root, "correctness_weight_5")
        all_jobs = jobs_cw1 + jobs_cw5

    if rank == 0:
        out_root.mkdir(parents=True, exist_ok=True)
        (out_root / "rollouts").mkdir(parents=True, exist_ok=True)
        (out_root / "full_outputs").mkdir(parents=True, exist_ok=True)
        if permanent_root:
            print(f"PERMANENT_ROOT: {resolve_repo_path(permanent_root)} ({len(all_jobs)} checkpoints)")
            for run_label, step, _, _, name in all_jobs:
                print(f"  {run_label} step={step} ({name})")
        else:
            print(f"Found {len(jobs_cw1)} checkpoints (cw=1), {len(jobs_cw5)} checkpoints (cw=5)")
            for jl in (jobs_cw1, jobs_cw5):
                for run_label, step, _, _, name in jl:
                    print(f"  {run_label} step={step} ({name})")

    if dist.is_initialized():
        dist.barrier()

    rows: list[dict] = []
    for run_label, step, _resolved_str, resolved_path, dir_name in all_jobs:
        records = evaluate_one_model(
            model_dir=resolved_path,
            base_cfg=base_cfg,
            eval_max_samples=eval_max_samples,
            batch_size=eval_batch_size,
        )
        if rank == 0:
            acc = sum(float(r["correctness"]) for r in records) / len(records) if records else 0.0
            avg_cot = sum(float(r["cot_words"]) for r in records) / len(records) if records else 0.0
            row = {
                "run_label": run_label,
                "checkpoint_step": step,
                "checkpoint_dir": dir_name,
                "model_dir": str(resolved_path),
                "num_examples": len(records),
                "accuracy": acc,
                "avg_cot_words": avg_cot,
            }
            rows.append(row)

            rollout_dir = out_root / "rollouts" / run_label
            rollout_dir.mkdir(parents=True, exist_ok=True)
            rollout_path = rollout_dir / f"{dir_name}_rollouts.jsonl"
            with rollout_path.open("w", encoding="utf-8") as handle:
                for rec in records[:rollout_n]:
                    handle.write(json.dumps(rec, ensure_ascii=True) + "\n")

            full_path = out_root / "full_outputs" / run_label / f"{dir_name}_outputs.jsonl"
            full_path.parent.mkdir(parents=True, exist_ok=True)
            with full_path.open("w", encoding="utf-8") as handle:
                for rec in records:
                    handle.write(json.dumps(rec, ensure_ascii=True) + "\n")

            print(
                f"Eval {run_label} {dir_name}: acc={acc:.4f} avg_cot_words={avg_cot:.2f} n={len(records)}"
            )

        if dist.is_initialized():
            dist.barrier()

    if rank != 0:
        return

    rows.sort(key=lambda r: (r["run_label"], r["checkpoint_step"], r["checkpoint_dir"]))

    summary_json = out_root / "permanent_checkpoints_eval.json"
    summary_csv = out_root / "permanent_checkpoints_eval.csv"
    summary_json.write_text(json.dumps(rows, indent=2), encoding="utf-8")
    with summary_csv.open("w", encoding="utf-8", newline="") as handle:
        w = csv.DictWriter(
            handle,
            fieldnames=[
                "run_label",
                "checkpoint_step",
                "checkpoint_dir",
                "model_dir",
                "num_examples",
                "accuracy",
                "avg_cot_words",
            ],
        )
        w.writeheader()
        for row in rows:
            w.writerow(row)

    def series_for(label: str, ykey: str) -> list[tuple[int, float]]:
        return [
            (int(r["checkpoint_step"]), float(r[ykey]))
            for r in rows
            if r["run_label"] == label
        ]

    palette = ["#2563eb", "#dc2626", "#16a34a", "#9333ea", "#ca8a04", "#0891b2"]
    uniq_labels = sorted({str(r["run_label"]) for r in rows})
    acc_series = [
        (lab, series_for(lab, "accuracy"), palette[i % len(palette)])
        for i, lab in enumerate(uniq_labels)
        if series_for(lab, "accuracy")
    ]
    cot_series = [
        (lab, series_for(lab, "avg_cot_words"), palette[i % len(palette)])
        for i, lab in enumerate(uniq_labels)
        if series_for(lab, "avg_cot_words")
    ]

    cot_max = 1.0
    for r in rows:
        cot_max = max(cot_max, float(r["avg_cot_words"]))

    if acc_series:
        _line_chart_svg(
            acc_series,
            "GSM8K accuracy vs checkpoint step",
            "Accuracy",
            1.0,
            out_root / "accuracy_vs_step.svg",
        )
    if cot_series:
        _line_chart_svg(
            cot_series,
            "Average CoT length (words) vs checkpoint step",
            "Avg CoT words",
            cot_max,
            out_root / "avg_cot_vs_step.svg",
        )

    _scatter_accuracy_vs_cot_svg(
        rows,
        out_root / "accuracy_vs_avg_cot_words.svg",
        "GSM8K accuracy vs average CoT length (words)",
    )

    print(f"Saved: {summary_json}")
    print(f"Saved: {summary_csv}")
    if acc_series:
        print(f"Saved: {out_root / 'accuracy_vs_step.svg'}")
    if cot_series:
        print(f"Saved: {out_root / 'avg_cot_vs_step.svg'}")
    print(f"Saved: {out_root / 'accuracy_vs_avg_cot_words.svg'}")
    print(f"Rollouts: {out_root / 'rollouts'}/<run_label>/")
    print(f"Full outputs: {out_root / 'full_outputs'}/<run_label>/")


if __name__ == "__main__":
    main()